Primary prevention of hepatic encephalopathy post-TIPS: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Transjugular intrahepatic portosystemic shunt (TIPS) can be an effective treatment for cirrhotic patients who develop variceal bleeding and ascites. However, TIPS placement is associated with an increased risk of developing hepatic encephalopathy (HE). Recently, there have been efforts to use the typical medical therapies prophylactically in patients undergoing TIPS placement to prevent post-TIPS HE. METHODS: We conducted literature searches in MEDLINE, Embase, CINAHL, Scopus, and Cochrane to examine studies that use prophylactic medical therapy for preventing post-TIPS HE. A narrative synthesis and grading of recommendations assessment assessment were done for all studies. Meta-analysis was performed for eligible studies using the Mantel-Haenszel method random-effects model. Nine hundred twenty-one articles were screened and 5 studies were included in the study after 2 levels of screening. The medications studied were rifaximin, lactulose, lactitol, L-Ornithine-L-aspartate (LOLA), albumin, and combination therapies. RESULTS: Narrative results showed that lactulose, lactitol, LOLA and albumin prophylaxis were not associated with reduction in HE occurrence or mortality. A combination of rifaximin and lactulose was found to be associated with lower occurrence of HE, and the results were not different when LOLA was added. Meta-analysis (n = 3) showed that rifaximin treatment was not associated with changes in HE occurrences. CONCLUSION: In conclusion, a vast majority of medications were not found to be effective post-TIPS HE prophylaxis when used alone. A rifaximin and lactulose combination therapy may be beneficial. Overall, there is significant limitation in the current data and more studies are needed to yield more robust meta-analysis results in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".